Multiharmonic tracking using marginalized particle filters.

Man-made and natural systems often generate signals with multi-harmonic components, and the accurate estimation of the harmonically related components of these signals is critical for various applications. The posterior distribution of frequency estimates for this class of signal is multi-model--posing a challenge for frequency tracking algorithms which may lock onto a super or sub harmonic of the fundamental frequency. We propose a multi-harmonic tracker based on a sequential Monte Carlo method (SMCM) which can account for the multi-modality of the posterior distribution to track the harmonically related components of a signal more accurately than a tracker based on local linearization. We compare the SMCM multi-harmonic tracker with the extended Kalman filter (EKF) multi-harmonic tracker by applying them to real biomedical signals including electrocardiograms (ECG) and arterial blood pressure (ABP) signals. The results clearly show the superior performance of the proposed multi-harmonic tracker over the EKF tracker.